Search results for: deep convolutional features

Authors: S. Mani, M. Kafil, E. Asadi

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Condition Monitoring (CM) of electrical equipment has gained remarkable importance during the recent years; due to huge production losses, substantial imposed costs and increases in vulnerability, risk and uncertainty levels. Power cables feed numerous electrical equipment such as transformers, motors, and electric furnaces; thus their condition assessment is of a very great importance. This paper investigates electrical, structural and environmental failure sources, all of which influence cables' performances and limit their uptimes; and provides a comprehensive framework entailing practical CM guidelines for maintenance of cables in industries. The multilevel CM framework presented in this study covers performance indicative features of power cables; with a focus on both online and offline diagnosis and test scenarios, and covers short-term and long-term threats to the operation and longevity of power cables. The study, after concisely overviewing the concept of CM, thoroughly investigates five major areas of power quality, Insulation Quality features of partial discharges, tan delta and voltage withstand capabilities, together with sheath faults, shield currents and environmental features of temperature and humidity; and elaborates interconnections and mutual impacts between those areas; using mathematical formulation and practical guidelines. Detection, location, and severity identification methods for every threat or fault source are also elaborated. Finally, the comprehensive, practical guidelines presented in the study are presented for the specific case of Electric Arc Furnace (EAF) feeder MV power cables in Mobarakeh Steel Company (MSC), the largest steel company in MENA region, in Iran. Specific technical and industrial characteristics and limitations of a harsh industrial environment like MSC EAF feeder cable tunnels are imposed on the presented framework; making the suggested package more practical and tangible.

Keywords: condition monitoring, diagnostics, insulation, maintenance, partial discharge, power cables, power quality

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Authors: Mazouz Halima, Belghachi Abdrahmane

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Effects of electron irradiation-induced deep level defects have been studied on both n/p and p/n indium phosphide solar cells with very thin emitters. The simulation results show that n/p structure offers a somewhat better short circuit current but the p/n structure offers improved circuit voltage, not only before electron irradiation, but also after 1MeV electron irradiation with 5.1015 fluence. The simulation also shows that n/p solar cell structure is more resistant than that of p/n structure.

Keywords: InP solar cell, p/n and n/p structure, electron irradiation, output parameters

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Authors: R. R. Ramsheeja, R. Sreeraj

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For view the internal structures of the human body such as liver, brain, kidney etc have a wide range of different modalities for medical images are provided nowadays. Computer Tomography is one of the most significant medical image modalities. In this paper use CT liver images for study the use of automatic computer aided techniques to calculate the volume of the liver tumor. Segmentation method is used for the detection of tumor from the CT scan is proposed. Gaussian filter is used for denoising the liver image and Adaptive Thresholding algorithm is used for segmentation. Multiple Region Of Interest(ROI) based method that may help to characteristic the feature different. It provides a significant impact on classification performance. Due to the characteristic of liver tumor lesion, inherent difficulties appear selective. For a better performance, a novel proposed system is introduced. Multiple ROI based feature selection and classification are performed. In order to obtain of relevant features for Support Vector Machine(SVM) classifier is important for better generalization performance. The proposed system helps to improve the better classification performance, reason in which we can see a significant reduction of features is used. The diagnosis of liver cancer from the computer tomography images is very difficult in nature. Early detection of liver tumor is very helpful to save the human life.

Keywords: computed tomography (CT), multiple region of interest(ROI), feature values, segmentation, SVM classification

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Authors: A. V. Harutyunyan

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The investigation results of rocks at high pressures and temperatures have revealed the intervals of changes of seismic waves and density, as well as some processes taking place in rocks. In the serpentinized rocks, as a consequence of dehydration, abrupt changes in seismic waves and density have been recorded. Hydrogen-bearing components are released which combine with carbon-bearing components. As a result, hydrocarbons formed. The investigated samples are smelted. Then, geofluids and hydrocarbons migrate into the upper horizons of the Earth crust by the deep faults. Then their differentiation and accumulation in the jointed rocks of the faults and in the layers with collecting properties takes place. Under the majority of the hydrocarbon deposits, at a certain depth, magmatic centers and deep faults are recorded. The investigation results of the serpentinized rocks with numerous geological-geophysical factual data allow understanding that hydrocarbons are mainly formed in both the offshore part of the ocean and at different depths of the continental crust. Experiments have also shown that the dehydration of the serpentinized rocks is accompanied by an explosion with the instantaneous increase in pressure and temperature and smelting the studied rocks. According to numerous publications, hydrocarbons and diamonds are formed in the upper part of the mantle, at the depths of 200-400km, and as a consequence of geodynamic processes, they rise to the upper horizons of the Earth crust through narrow channels. However, the genesis of metamorphogenic diamonds and the diamonds found in the lava streams formed within the Earth crust, remains unclear. As at dehydration, super high pressures and temperatures arise. It is assumed that diamond crystals are formed from carbon containing components present in the dehydration zone. It can be assumed that besides the explosion at dehydration, secondary explosions of the released hydrogen take place. The process is naturally accompanied by seismic phenomena, causing earthquakes of different magnitudes on the surface. As for the diamondiferous kimberlites, it is well-known that the majority of them are located within the ancient shield and platforms not obligatorily connected with the deep faults. The kimberlites are formed at the shallow location of dehydrated masses in the Earth crust. Kimberlites are younger in respect of containing ancient rocks containing serpentinized bazites and ultrbazites of relicts of the paleooceanic crust. Sometimes, diamonds containing water and hydrocarbons showing their simultaneous genesis are found. So, the geofluids, hydrocarbons and diamonds, according to the new concept put forward, are formed simultaneously from serpentinized rocks as a consequence of their dehydration at different depths of the Earth crust. Based on the concept proposed by us, we suggest discussing the following: -Genesis of gigantic hydrocarbon deposits located in the offshore area of oceans (North American, Mexican Gulf, Cuanza-Kamerunian, East Brazilian etc.) as well as in the continental parts of different mainlands (Kanadian-Arctic Caspian, East Siberian etc.) - Genesis of metamorphogenic diamonds and diamonds in the lava streams (Guinea-Liberian, Kokchetav, Kanadian, Kamchatka-Tolbachinian, etc.).

Keywords: dehydration, diamonds, hydrocarbons, serpentinites

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Ponghyung Lee

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This study seeks to investigate the variations occurring to Korean consonantal variations center around laryngeal features of the concerned sounds, to the exclusion of others. Our fundamental premise is that the weak contrast associated with concerned segments might be held accountable for the oscillation of the status quo of the concerned consonants. What is more, we assume that an array of notions as a measure of communicative efficiency of linguistic units would be significantly influential on triggering those variations. To this end, we have tried to compute the surprisal, entropic contribution, and relative contrastiveness associated with Korean obstruent consonants. What we found therein is that the Information-theoretic perspective is compelling enough to lend support our approach to a considerable extent. That is, the variant realizations, chronologically and stylistically, prove to be profoundly affected by a set of Information-theoretic factors enumerated above. When it comes to the biblical proper names, we use Georgetown University CQP Web-Bible corpora. From the 8 texts (4 from Old Testament and 4 from New Testament) among the total 64 texts, we extracted 199 samples. We address the issue of laryngeal feature variations associated with Korean obstruent consonants under the presumption that the variations stem from the weak contrast among the triad manifestations of laryngeal features. The variants emerge from diverse sources in chronological and stylistic senses: Christianity biblical texts, ordinary casual speech, the shift of loanword adaptation over time, and ideophones. For the purpose of discussing what they are really like from the perspective of Information Theory, it is necessary to closely look at the data. Among them, the massive changes occurring to loanword adaptation of proper nouns during the centennial history of Korean Christianity draw our special attention. We searched 199 types of initially capitalized words among 45,528-word tokens, which account for around 5% of total 901,701-word tokens (12,786-word types) from Georgetown University CQP Web-Bible corpora. We focus on the shift of the laryngeal features incorporated into word-initial consonants, which are available through the two distinct versions of Korean Bible: one came out in the 1960s for the Protestants, and the other was published in the 1990s for the Catholic Church. Of these proper names, we have closely traced the adaptation of plain obstruents, e. g. /b, d, g, s, ʤ/ in the sources. The results show that as much as 41% of the extracted proper names show variations; 37% in terms of aspiration, and 4% in terms of tensing. This study set out in an effort to shed light on the question: to what extent can we attribute the variations occurring to the laryngeal features associated with Korean obstruent consonants to the communicative aspects of linguistic activities? In this vein, the concerted effects of the triad, of surprisal, entropic contribution, and relative contrastiveness can be credited with the ups and downs in the feature specification, despite being contentiousness on the role of surprisal to some extent.

Keywords: entropic contribution, laryngeal feature variation, relative contrastiveness, surprisal

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Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Umbreen Salim, Anila Noor

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This paper analyses the influences and faces of colonialism in women’s participation in economic activity in postcolonial Pakistan, through postcolonial feminist economic lens. It is an attempt to probe the shifts in gender inequalities that have existed in three stages; pre-colonial, colonial, and postcolonial times in the Indo-Pak subcontinent. It delves into an inquiry of pre-colonial as it is imperative to understand the situation and context before colonisation in order to assess the deviations associated with its onset. Hence, in order to trace gender inequalities this paper analyses from Mughal Era (1526-1757) that existed before British colonisation, then, the gender inequalities that existed during British colonisation (1857- 1947) and the associated dynamics and changes in women’s vulnerabilities to participate in the economy are examined. Followed by, the postcolonial (1947 onwards) scenario of discriminations and oppressions faced by women. As part of the research methodology, primary and secondary data analysis was done. Analysis of secondary data including literary works and photographs was carried out, followed by primary data collection using ethnographic approaches and participatory tools to understand the presence of coloniality and gender inequalities embedded in the social structure through participant’s real-life stories. The data is analysed using feminist postcolonial analysis. Intersectionality has been a key tool of analysis as the paper delved into the gender inequalities through the class and caste lens briefly touching at religion. It is imperative to mention the significance of the study and very importantly the practical challenges as historical analysis of 18th and 19th century is involved. Most of the available work on history is produced by a) men and b) foreigners and mostly white authors. Since the historical analysis is mostly by men the gender analysis presented misses on many aspects of women’s issues and since the authors have been mostly white European gives it as Mohanty says, ‘under western eyes’ perspective. Whereas the edge of this paper is the authors’ deep attachment, belongingness as lived reality and work with women in Pakistan as postcolonial subjects, a better position to relate with the social reality and understand the phenomenon. The study brought some key results as gender inequalities existed before colonisation when women were hidden wheel of stable economy which was completely invisible. During the British colonisation, the vulnerabilities of women only increased and as compared to men their inferiority status further strengthened. Today, the postcolonial woman lives in deep-rooted effects of coloniality where she is divided in class and position within the class, and she has to face gender inequalities within household and in the market for economic participation. Gender inequalities have existed in pre-colonial, during colonisation and postcolonial times in Pakistan with varying dynamics, degrees and intensities for women whereby social class, caste and religion have been key factors defining the extent of discrimination and oppression. Colonialism may have physically ended but the coloniality remains and has its deep, broad and wide effects in increasing gender inequalities in women’s participation in the economy in Pakistan.

Keywords: colonialism, economic participation, gender inequalities, women

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Authors: Juan C. Sanchez, Jose L. Endrino, Alejandro Toro, Hugo A. Estupinan, Glenn Leighton

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For years, the reduction of friction and wear has been a matter of interest in the engineering field. Several solutions have been proposed to address this issue, including the use of lubricants and coatings to reduce the frictional forces and to increase the surface wear resistance. Alternatively, texturing processes have been used in a wide variety of materials, in many cases inspired in natural surfaces. Nature has shown how species adapt to the environment and the engineers try to understand natural surfaces for particular applications by analyzing outstanding species such as gecko for high adhesion, lotus leaves for hydrophobicity, sharks for reduced flow resistance and snakes for optimized frictional response. Texturized surfaces have shown a superior performance in terms of the frictional response in many situations, and the control of its behavior greatly depends on the manufacturing process. The focus of this work is to evaluate the tribological behavior of AISI 52100 steel samples texturized by Photochemical Machining (PCM). The surface texture was inspired by several features of the snakeskin such as aspect ratio of fibrils and mean fibril spacing. Two coatings were applied on the texturized surface, namely Diamond-like Carbon (DLC) and Molybdenum Disulphide (MoS₂), and their tribological behavior after pin-on-disk tests were compared with that of the non-texturized and uncovered surfaces. The samples were characterised through Stereoscopic Microscope (SM), Scanning Electron Microscope (SEM), Optical Microscope (OM), Profilometer, Raman Spectrometer (RS) and X-Ray Diffractometer (XRD). The Coefficient of Friction (COF) measured in pin-on-disk tests showed correlations with the sliding direction (relative to the texture features) and the aspect ratio of the texture features. Regarding the coated surfaces, the DLC and MoS₂ coating had a good performance in terms of wear rate and coefficient of friction compared with the uncoated and non-texturized surfaces. On the other hand, for the uncoated surfaces, the texture showed an influence in the tribological performance with respect to the non-texturized surface.

Keywords: coating, coefficient of friction, deterministic surface, photochemical machining

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Authors: Anne M. Denton, Rahul Gomes, David W. Franzen

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High-resolution elevation data are becoming increasingly available, but typical approaches for computing topographic features, like slope and curvature, still assume small sliding windows, for example, of size 3x3. That means that the digital elevation model (DEM) has to be resampled to the scale of the landform features that are of interest. Any higher resolution is lost in this resampling. When the topographic features are computed through regression that is performed at the resolution of the original data, the accuracy can be much higher, and the reported result can be adjusted to the length scale that is relevant locally. Slope and variance are calculated for overlapping windows, meaning that one regression result is computed per raster point. The number of window centers per area is the same for the output as for the original DEM. Slope and variance are computed by performing regression on the points in the surrounding window. Such an approach is computationally feasible because of the additive nature of regression parameters and variance. Any doubling of window size in each direction only takes a single pass over the data, corresponding to a logarithmic scaling of the resulting algorithm as a function of the window size. Slope and variance are stored for each aggregation step, allowing the reported slope to be selected to minimize variance. The approach thereby adjusts the effective window size to the landform features that are characteristic to the area within the DEM. Starting with a window size of 2x2, each iteration aggregates 2x2 non-overlapping windows from the previous iteration. Regression results are stored for each iteration, and the slope at minimal variance is reported in the final result. As such, the reported slope is adjusted to the length scale that is characteristic of the landform locally. The length scale itself and the variance at that length scale are also visualized to aid in interpreting the results for slope. The relevant length scale is taken to be half of the window size of the window over which the minimum variance was achieved. The resulting process was evaluated for 1-meter DEM data and for artificial data that was constructed to have defined length scales and added noise. A comparison with ESRI ArcMap was performed and showed the potential of the proposed algorithm. The resolution of the resulting output is much higher and the slope and aspect much less affected by noise. Additionally, the algorithm adjusts to the scale of interest within the region of the image. These benefits are gained without additional computational cost in comparison with resampling the DEM and computing the slope over 3x3 images in ESRI ArcMap for each resolution. In summary, the proposed approach extracts slope and aspect of DEMs at the lengths scales that are characteristic locally. The result is of higher resolution and less affected by noise than existing techniques.

Keywords: high resolution digital elevation models, multi-scale analysis, slope calculation, window-based regression

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Authors: Moharram Sharifi

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The purpose of this study was to show how expert and novice writers take positions and stances in Research Articles and Master of Art theses Introductions, so Engagement resources were investigated in 30 Research Articles and 30 Master of Art theses written by Iranian non-native speakers. Through paired samples t-test analysis, we found out that the mean occurrences of heteroglossic items in both RA and Master thesis Introductions were larger than those of monoglossic items, indicating the awareness of both groups of writers to ‘engage’ alternative positions in Introduction sections. The results also revealed that expansive choices were preferred over contractive options in both corpora, implying both groups of writers respect alternative voices cautiously by welcoming rather than closing down the possibility of different perspectives and stances. Furthermore, unlike novice academic writers who used more Attribute features than Entertainment ones in their MATs introduction sections, expert academic writers employed a balanced number of Entertainment and Attribute in their RA introduction sections. The balanced deployment of entertaining and Attribute features in RA Introductions by expert writers might be characteristics of the writers’ demonstration of politeness, which is commonly accepted as an essential feature in academic writing discourse. Finally, through qualitative analysis, it was demonstrated that MAT writers, as novice academic writers, suffered from lacking appropriate evaluative stances and authorial voices toward propositions.

Keywords: novice, expert, engagement, RA Introductions, MA Thesis

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Authors: Hooshag Asadi Haroni, Maryam Veiskarami, Yongjun Lu

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The Dalli gold-rich porphyry deposit (17 Mt @ 0.5% Cu and 0.65 g/t Au) is located in the Urumieh-Dokhtar Magmatic Arc (UDMA), a small segment of the Tethyan metallogenic belt, hosting several porphyry Cu (Mo-Au) systems in Iran. This research characterizes the Dalli deposit to define exploration criteria in advanced exploration such as the drilling of possible blind porphyry centers. Geological map, trench/drill hole geochemical and ground magnetic data, and age dating and isotope trace element analyses, carried out at the John De Laeter Research Center of Curtin University, were used to characterize the Delli deposit. Mineralization at Dalli is hosted by NE-trending quartz-diorite porphyry stocks (~ 200m in diameter) intruded by a wall-rock andesite porphyry. Disseminated and stockwork Cu-Au mineralization is related to potassic alteration, comprising magnetite, late K-feldspar and biotite, and quartz-sericite-specularite overprint, surrounded by extensive barren argillic and propylitic alterations. In the peripheries of the porphyry centers, there are N-trending vuggy quartz veins, hosting epithermal Au-Ag-As-Sb mineralization. Geochemical analyses of drill core samples showed that the core of the porphyry stocks is low-grade, whereas the high-grade disseminated and stockwork mineralization (~ 1% Cu and ~ 1.2 g/t Au) occurred at the contact of the porphyry stocks and andesite porphyry. Geochemical studies of the drill hole and trench samples showed a strong correlation between Cu and Au and both show a second-order correlation with Fe and As. Magnetic survey revealed two significant magnetic anomalies, associated with intensive potassic alteration, in the reduced-to-the-pole magnetic map of the area. A relatively weaker magnetic anomaly, showing no surface porphyry expressions, is located on a lithocap, consisting of advanced argillic alteration, vuggy quartz veins, and surface expressions of epithermal geochemical signatures. The association of the lithocap and the weak magnetic anomaly could be indicative of a hidden mineralized porphyry center. Litho-geochemical analyses of the least altered Dalli intrusions and volcanic rocks indicated high Sr/Y (49-61) and Eu/Eu* (0.89-0.92), features typical of Cu porphyries. The U-Pb dating of zircons of the mineralized quartz diorite and andesite porphyry, carried out by laser ablation inductively coupled plasma mass spectrometry, yielded magmatic crystallization ages of 15.4-16.0 Ma (Middle Miocene). The zircon trace element concentrations of Dalli are characterized by high Eu/Eu* (0.3-0.8), (Ce/Nd)/Y (0.01-0.3), and 10000*(Eu/Eu*)/Y (2-15) ratios, similar to fertile porphyry suites such as the giant Sar-Cheshmeh and Qulong porphyry Cu deposits along the Tethyan belt. This suggests that the Middle Miocene Dalli intrusions are fertile and require extensive deep drillings to define their potential. Chondrite-normalized rare earth element (REE) patterns show no significant Eu anomalies, and are characterized by light-REE enrichments (La/Sm)n = 2.57–6.40). In normalized multi-element diagrams, analyzed rocks are characterized by enrichments in large ion lithophile elements (LILE) and depletions in high field strength elements (HFSE), and display typical features of subduction-related calc-alkaline magmas. The characteristics of the Dalli deposit provided several recognition criteria for detailed exploration of Cu-Au porphyry deposits and highlighted the importance of the UDMA as a potentially significant, economically important, but relatively underexplored porphyry province.

Keywords: porphyry, gold, geochronology, magnetic, exploration

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Authors: R. Simões, M. Silveirinha

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Driven by the need to understand the results of previous research that clearly shows deep unbalances of the media discourses about women and men in spite of the growing numbers of female journalists, our paper aims to progress from the 'what' to the 'why' of these unbalanced representations. Furthermore, it does so at a time when journalism is undergoing a dramatic change in terms of professional practices and in how media organizations are organized and run, affecting women in particular. While some feminist research points to the fact that female and male journalists evaluate the role of the news and production methods in similar ways feminist theorizing also suggests that thought and knowledge are highly influenced by social identity, which is also inherently affected by the experiences of gender. This is particularly important at a time of deep societal and professional changes. While there are persuasive discussions of gender identities at work in newsrooms in various countries studies on the issue will benefit from cases that focus on the particularities of local contexts. In our paper, we present one such case: the case of Portugal, a country hit hard by austerity measures that have affected all cultural industries including journalism organizations, already feeling the broader impacts of the larger societal changes of the media landscape. Can we gender these changes? How are they felt and understood by female and male journalists? And how are these discourses framed by androcentric, feminist and post-feminist sensibilities? Foregrounding questions of gender, our paper seeks to explore some of the interactions of societal and professional forces, identifying their gendered character and outlining how they shape journalism work in general and the production of unbalanced gender representations in particular. We do so grounded in feminist studies of journalism as well as feminist organizational and work studies, looking at a corpus of 20 in-depth interviews of female and male Portuguese journalists. The research findings illustrate how gender in journalism practices interacts with broader experiences of the cultural and economic contexts and show the ambivalences of these interactions in news organizations.

Keywords: gender, journalism, newsroom culture, Portuguese journalists

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Authors: Bayar Shahab

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The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems, and issues of this new era have been found and are being found like no other time in history. Brain-computer interface so-called BCI has opened the door to several new research areas and have been able to provide solutions to critical and important issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair or even a car and neurotechnology enabled the rehabilitation of the lost memory, etc. This review work presents state-of-the-art methods and improvements of canonical correlation analyses (CCA), which is an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said in a different way, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers to understand the most state-of-the-art methods available in this field with their pros and cons, along with their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the prominent methods used in this field in a hierarchical way (2) explaining pros and cons of each method and their performance (3) presenting the gaps that exist at the end of each method that can open the understanding and doors to new research and/or improvements.

Keywords: BCI, CCA, SSVEP, EEG

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Authors: Yeliz Sarı Nayim, B. Niyami Nayim

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Biotopes such as forest areas rich in biodiversity, wetlands, hedgerows and woodlands play important ecological roles in agricultural landscapes. Of these semi-natural areas and features, hedgerows are the most common landscape elements. Their most significant features are that they serve as a barrier between the agricultural lands, serve as shelter, add aesthetical value to the landscape and contribute significantly to the wildlife and biodiversity. Hedgerows surrounding agricultural landscapes also provide an important habitat for pollinators which are important for agricultural production. This study looks into the identification of hedgerows in agricultural lands in the Mugada rural area within Bartın province, Turkey. From field data and-and satellite images, it is clear that in this area, especially around rural settlements, large forest areas have been cleared for settlement and agriculture. A network of hedgerows is also apparent, which might potentially play an important role in the otherwise open agricultural landscape. We found that these hedgerows serve as an ecological and biological corridor, linking forest ecosystems. Forest patches of different sizes and creating a habitat network across the landscape. Some examples of this will be presented. The overall conclusion from the study is that ecologically, biologically and aesthetically important hedge biotopes should be maintained in the long term in agricultural landscapes such as this. Some suggestions are given for how they could be managed sustainably into the future.

Keywords: agricultural biotopes, Hedgerows, landscape ecology, Turkey

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Kotaro Sasai, Daijiro Mizutani, Kiyoyuki Kaito

Abstract:

Expressways in Japan have been built in an accelerating manner since the 1960s with the aid of rapid economic growth. About 40 percent in length of expressways in Japan is now 30 years and older and has become superannuated. Time-related deterioration has therefore reached to a degree that administrators, from a standpoint of operation and maintenance, are forced to take prompt measures on a large scale aiming at repairing inner damage deep in pavements. These measures have already been performed for bridge management in Japan and are also expected to be embodied for pavement management. Thus, planning methods for the measures are increasingly demanded. Deterioration of layers around road surface such as surface course and binder course is brought about at the early stages of whole pavement deterioration process, around 10 to 30 years after construction. These layers have been repaired primarily because inner damage usually becomes significant after outer damage, and because surveys for measuring inner damage such as Falling Weight Deflectometer (FWD) survey and open-cut survey are costly and time-consuming process, which has made it difficult for administrators to focus on inner damage as much as they have been supposed to. As expressways today have serious time-related deterioration within them deriving from the long time span since they started to be used, it is obvious the idea of repairing layers deep in pavements such as base course and subgrade must be taken into consideration when planning maintenance on a large scale. This sort of maintenance requires precisely predicting degrees of deterioration as well as grasping the present situations of pavements. Methods for predicting deterioration are determined to be either mechanical or statistical. While few mechanical models have been presented, as far as the authors know of, previous studies have presented statistical methods for predicting deterioration in pavements. One describes deterioration process by estimating Markov deterioration hazard model, while another study illustrates it by estimating Proportional deterioration hazard model. Both of the studies analyze deflection data obtained from FWD surveys and present statistical methods for predicting deterioration process of layers around road surface. However, layers of base course and subgrade remain unanalyzed. In this study, data collected from FWD surveys are analyzed to predict deterioration process of layers deep in pavements in addition to surface layers by a means of estimating a deterioration hazard model using continuous indexes. This model can prevent the loss of information of data when setting rating categories in Markov deterioration hazard model when evaluating degrees of deterioration in roadbeds and subgrades. As a result of portraying continuous indexes, the model can predict deterioration in each layer of pavements and evaluate it quantitatively. Additionally, as the model can also depict probability distribution of the indexes at an arbitrary point and establish a risk control level arbitrarily, it is expected that this study will provide knowledge like life cycle cost and informative content during decision making process referring to where to do maintenance on as well as when.

Keywords: deterioration hazard model, falling weight deflectometer, inner damage, load bearing capacity, pavement

Procedia PDF Downloads 390

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Harish Rajak, Preeti Patel

Abstract:

HDAC inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. The 3D-QSAR and Pharmacophore modeling studies were performed to identify important pharmacophoric features and correlate 3D-chemical structure with biological activity. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with well-assigned HDAC inhibitory activity was used for 3D-QSAR model development. Best 3D-QSAR model, which is a five PLS factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811) and standard deviation (0.0952). Molecular docking were performed using Histone Deacetylase protein (PDB ID: 1t69) and prepared series of hydroxamic acid based HDAC inhibitors. Docking study of compound 43 show significant binding interactions Ser 276 and oxygen atom of dioxine cap region, Gly 151 and amino group and Asp 267 with carboxyl group of CONHOH, which are essential for anticancer activity. On docking, most of the compounds exhibited better glide score values between -8 to -10.5. We have established structure activity correlation using docking, energetic based pharmacophore modelling, pharmacophore and atom based 3D QSAR model. The results of these studies were further used for the design and testing of new HDAC analogs.

Keywords: Docking, e-pharmacophore, HDACIs, QSAR, Suberoylanilidehydroxamic acid.

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Authors: Zixi You

Abstract:

There are a number of verbal prefixes in Old Japanese. However, the selection or the compatibility of verbs and verbal prefixes is among the least investigated topics on Old Japanese language. Unlike other types of prefixes, verbal prefixes in dictionaries are more often than not listed with very brief information such as ‘unknown meaning’ or ‘rhythmic function only’. To fill in a part of this knowledge gap, this paper presents an exhaustive investigation based on the newly developed ‘Oxford Corpus of Old Japanese’ (OCOJ), which included nearly all existing resource of Old Japanese language, with detailed linguistics information in TEI-XML tags. In this paper, we propose the possibility that the following three prefixes, i-, sa-, ta- (with ta- being considered as a variation of sa-), are relevant to split intransitivity in Old Japanese, with evidence that unergative verbs favor i- and that unergative verbs favor sa-(ta-). This might be undermined by the fact that transitives are also found to follow i-. However, with several manifestations of split intransitivity in Old Japanese discussed, the behavior of transitives in verbal prefix selection is no longer as surprising as it may seem to be when one look at the selection of verbal prefix in isolation. It is possible that there are one or more features that played essential roles in determining the selection of i-, and the attested transitive verbs happen to have these features. The data suggest that this feature is a sense of ‘change’ of location or state involved in the event donated by the verb, which is a feature of typical unaccusatives. This is further discussed in the ‘affectedness’ hierarchy. The presentation of this paper, which includes a brief demonstration of the OCOJ, is expected to be of the interest of both specialists and general audiences.

Keywords: old Japanese, split intransitivity, unaccusatives, unergatives, verbal prefix selection

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Authors: Ignacio W. Loor

Abstract:

In the context of informal settlements in Quito, this paper provides evidence that slopes and deep ravines typical of Andean cities, around which marginalized urban communities sit, constitute a platform for green infrastructure that supports mobility for pedestrians in an incremental fashion. This is informally shaped green infrastructure that provides connectivity to other mobility infrastructures such as roads and public transport, which permits relegated dwellers reach their daily destinations and reclaim their rights to the city. This is relevant in that walking has been increasingly neglected as a viable mean of transport in Latin American cities, in favor of rather motorized means, for which the mobility benefits of green infrastructure have remained invisible to policymakers, contributing to the progressive isolation of informal settlements. This research leverages greatly on an ecological rejuvenation programme led by the municipality of Quito and the Andean Corporation for Development (CAN) intended for rehabilitating the ecological functionalities of ravines. Accordingly, four ravines in different stages of rejuvenation were chosen, in order to through ethnographic methods, capture the practices they support to dwellers of informal settlements across different stages, particularly in terms of issues of mobility. Then, by presenting fragments of interviews, description of observed phenomena, photographs and narratives published in institutional reports and media, the production process of mobility infrastructure over unoccupied slopes and ravines, and the roles that this infrastructure plays in the mobility of dwellers and their quotidian practices are explained. For informal settlements, which normally feature scant urban infrastructure, mobility embodies an unfavourable driver for the possibilities of dwellers to actively participate in the social, economic and political dimensions of the city, for which their rights to the city are widely neglected. Nevertheless, informal green infrastructure for mobility provides some alleviation. This infrastructure is incremental, since its features and usability gradually evolves as users put into it knowledge, labour, devices, and connectivity to other infrastructures in different dimensions which increment its dependability. This is evidenced in the diffusion of knowledge of trails and routes of footpaths among users, the implementation of linking stairs and bridges, the improved access by producing public spaces adjacent to the ravines, the illuminating of surrounding roads, and ultimately, the restoring of ecological functions of ravines. However, the perpetuity of this type of infrastructure is also fragile and vulnerable to the course of urbanisation, densification, and expansion of gated privatised spaces.

Keywords: green infrastructure, informal settlements, urban mobility, walkability

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Authors: Faizan Tariq, Moayid Ali Zaidi

Abstract:

Facial emotion recognition algorithms are expanding rapidly now a day. People are using different algorithms with different combinations to generate best results. There are six basic emotions which are being studied in this area. Author tried to recognize the facial expressions using object detector algorithms instead of traditional algorithms. Two object detection algorithms were chosen which are Faster R-CNN and YOLO. For pre-processing we used image rotation and batch normalization. The dataset I have chosen for the experiments is Static Facial Expression in Wild (SFEW). Our approach worked well but there is still a lot of room to improve it, which will be a future direction.

Keywords: face recognition, emotion recognition, deep learning, CNN

Procedia PDF Downloads 187

Authors: Lei Zhang

Abstract:

The carbon cycle greatly influences global change, and the land cover changes contribute to the status and rate of the carbon budget in ecosystems. This paper proposes a land cover classification system for mapping land cover, the national ecological environment assessment, and estimating carbon storage in ecosystems. The classification system consists of basic land cover classes at levels Ⅰ and Ⅱ and auxiliary features at level III. The basic 38 classes characterizing land cover features are derived from 19 criteria referring to composition, structure, pattern, phenology, etc. The basic classes reflect the status of carbon storage in ecosystems. The auxiliary classes at level III complement the attributes of higher levels by 9 criteria. The 5 environmental criteria of temperature, moisture, landform, aspect and slope mainly reflect the potential and intensity of carbon storage in ecosystems. The disturbance of vegetation succession caused by land use type influences the vegetation carbon budget. The other 3 vegetation cover criteria, growth period, and species characteristics further refine the vegetation types. The hierarchical structure of the land cover map (the classes of levels Ⅰ and Ⅱ) is independent of the products of level III, which is helpful for land cover product management and applications. The classification system has been adopted in the Chinese national land cover database for the carbon budget in ecosystems at a 30 m scale.

Keywords: classification system, land cover, ecosystem, carbon storage, object based

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Authors: Majid Rasta, Xiaotao Shi, Mian Adnan Kakakhel, Yanqin Bai, Lao Liu, Jia Manke

Abstract:

Microplastics (MPs; particles <5 mm) pollution is considered as a globally pervasive threat to aquatic ecosystems, and many studies reported this pollution in rivers, wetlands, lakes, coastal waters and oceans. In the aquatic environments, settling and transport of MPs in water column and sediments are determined by different factors such as hydrologic characteristics, watershed pattern, rainfall events, hydraulic conditions, vegetation, hydrodynamics behavior of MPs, and physical features of particles (shape, size and density). In the meantime, hydraulic conditions (such as turbulence, high/low water speed flows or water stagnation) play a key role in the fate of MPs in aquatic ecosystems. Therefore, this study presents a briefly review on the effects of different hydraulic conditions on the fate, transport and accumulation of MPs in aquatic ecosystems. Generally, MPs are distributed horizontally and vertically in aquatic environments. The vertical distribution of MPs in the water column changes with different flow velocities. In the riverine, turbulent flow causing from the rapid water velocity and shallow depth may create a homogeneous mixture of MPs throughout the water column. While low velocity followed by low-turbulent waters can lead to the low level vertical mixing of MP particles in the water column. Consequently, the high numbers of MPs are expected to be found in the sediments of deep and wide channels as well as estuaries. In contrast, observing the lowest accumulation of MP particles in the sediments of straights of the rivers, places with the highest flow velocity is understandable. In the marine environment, hydrodynamic factors (e.g., turbulence, current velocity and residual circulation) can affect the sedimentation and transportation of MPs and thus change the distribution of MPs in the marine and coastal sediments. For instance, marine bays are known as the accumulation area of MPs due to poor hydrodynamic conditions. On the other hand, in the nearshore zone, the flow conditions are highly complex and dynamic. Experimental studies illustrated that maximum horizontal flow velocity in the sandy beach can predict the accumulation of MPs so that particles with high sinking velocities deposit in the lower water depths. As a whole, it can be concluded that the transport and accumulation of MPs in aquatic ecosystems are highly affected by hydraulic conditions. This study provided information about the impacts of hydraulic on MPs pollution. Further research on hydraulics and its relationship to the accumulation of MPs in aquatic ecosystems is needed to increase insights into this pollution.

Keywords: microplastics pollution, hydraulic, transport, accumulation

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Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

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Authors: Chia-Min Lai, Wan-Ching Lin, Hahn-Ming Lee, Ching-Hao Mao

Abstract:

As world wild internet has non-stop developments, making profit by lending registered domain names emerges as a new business in recent years. Unfortunately, the larger the market scale of domain lending service becomes, the riskier that there exist malicious behaviors or malwares hiding behind parked domains will be. Also, previous work for differentiating parked domain suffers two main defects: 1) too much data-collecting effort and CPU latency needed for features engineering and 2) ineffectiveness when detecting parked domains containing external links that are usually abused by hackers, e.g., drive-by download attack. Aiming for alleviating above defects without sacrificing practical usability, this paper proposes ParkedGuard as an efficient and accurate parked domain detector. Several scripting behavioral features were analyzed, while those with special statistical significance are adopted in ParkedGuard to make feature engineering much more cost-efficient. On the other hand, finding memberships between external links and parked domains was modeled as a graph mining problem, and a coarse-to-fine strategy was elaborately designed by leverage the graphical locality such that ParkedGuard outperforms the state-of-the-art in terms of both recall and precision rates.

Keywords: coarse-to-fine strategy, domain parking service, graphical locality analysis, parked domain

Procedia PDF Downloads 409

Authors: Pranjal Srivastava, Piyali Sengupta

Abstract:

The performance of marine drilling risers is crucial in the offshore oil and gas industry to ensure safe drilling operation with minimum downtime. Experimental investigations on marine drilling risers are limited in the literature owing to the expensive and exhaustive test setup required to replicate the realistic riser model and ocean environment in the laboratory. Therefore, this study presents an analytical model of marine drilling riser for determining its fragility under ocean environmental loading. In this study, the marine drilling riser is idealized as a continuous beam having a concentric circular cross-section. Hydrodynamic loading acting on the marine drilling riser is determined by Morison’s equations. By considering the equilibrium of forces on the marine drilling riser for the connected and normal drilling conditions, the governing partial differential equations in terms of independent variables z (depth) and t (time) are derived. Subsequently, the Runge Kutta method and Finite Difference Method are employed for solving the partial differential equations arising from the analytical model. The proposed analytical approach is successfully validated with respect to the experimental results from the literature. From the dynamic analysis results of the proposed analytical approach, the critical design parameters peak displacements, upper and lower flex joint rotations and von Mises stresses of marine drilling risers are determined. An extensive parametric study is conducted to explore the effects of top tension, drilling depth, ocean current speed and platform drift on the critical design parameters of the marine drilling riser. Thereafter, incremental dynamic analysis is performed to derive the fragility functions of shallow water and deep-water marine drilling risers under ocean environmental loading. The proposed methodology can also be adopted for downtime estimation of marine drilling risers incorporating the ranges of uncertainties associated with the ocean environment, especially at deep and ultra-deepwater.

Keywords: drilling riser, marine, analytical model, fragility

Procedia PDF Downloads 146

Authors: Precious Barnes, Abraham Mensah, Leonard Derkyi-Kwarteng, Benjamin Amoani, George Adjei, Ernest Adankwah, Faustina Pappoe, Kwabena Dankwah, Daniel Amoako-Sakyi, Samuel Victor Nuvor, Dorcas Obiri-Yeboah, Ewura Seidu Yahaya, Patrick Kafui Akakpo, Roland Osei Saahene

Abstract:

Context: Breast cancer is a prevalent and aggressive type of cancer among African women, with high mortality rates in Ghana. Nuclear factor kappa B (NF-kB) is a transcription factor that has been associated with tumor progression in breast cancer. However, there is a lack of published data on NF-kB in breast cancer patients in Ghana or other African countries. Research Aim: The aim of this study was to assess the prognostic significance of NF-kB (p65) expression and its association with various clinicopathological features in breast cancer patients at the Cape Coast Teaching Hospital in Ghana. Methodology: A total of 90 formalin-fixed breast cancer tissues and 15 normal breast tissues were used in this study. The expression level of NF-kB (p65) was examined using immunohistochemical techniques. Correlation analysis between NF-kB (p65) expression and clinicopathological features was performed using SPSS version 25. Findings: The study found that NF-kB (p65) was expressed in 86.7% of breast cancer tissues. There was a significant relationship between NF-kB (p65) expression and tumor grade, proliferation index (Ki67), and molecular subtype. High-level expression of NF-kB (p65) was more common in tumor grade 3 compared to grade 1, and Ki67 > 20 had higher expression of NF-kB (p65) compared to Ki67 ≤ 20. Triple-negative breast cancer patients had the highest overexpression of NF-kB (p65) compared to other molecular subtypes. There was no significant association between NF-kB (p65) expression and other clinicopathological parameters. Theoretical Importance: This study provides important insights into the expression of NF-kB (p65) in breast cancer patients in Ghana, particularly in relation to tumor grade and proliferation index. The findings suggest that NF-kB (p65) could serve as a potential biological marker for cancer stage, progression, prognosis and as a therapeutic target. Data Collection and Analysis Procedures: Formalin-fixed breast cancer tissues and normal breast tissues were collected and analyzed using immunohistochemical techniques. Correlation analysis between NF-kB (p65) expression and clinicopathological features was performed using SPSS version 25. Question Addressed: This study addressed the question of the prognostic significance of NF-kB (p65) expression and its association with clinicopathological features in breast cancer patients in Ghana. Conclusion: This study, the first of its kind in Ghana, demonstrates that NF-kB (p65) is highly expressed among breast cancer patients at the Cape Coast Teaching Hospital, especially in triple-negative breast cancer patients. The expression of NF-kB (p65) is associated with tumor grade and proliferation index. NF-kB (p65) could potentially serve as a biological marker for cancer stage, progression, prognosis, and as a therapeutic target.

Keywords: breast cancer, Ki67, NF-kB (p65), tumor grade

Procedia PDF Downloads 72